Bayesian model selection for meta-analysis of diagnostic test accuracy data: Application to Ddimer for deep vein thrombosis

Meta-analysis of the accuracy of two diagnostic tests used in combination: application to the ddimer test and the wells score for the diagnosis of deep vein thrombosis

OBJECTIVES

It is standard practice for diagnostic tests to be evaluated against gold standards in isolation. In routine clinical practice, however, it is commonplace for multiple tests to be used before making definitive diagnoses. This article describes a meta-analytic modeling framework developed to estimate the accuracy of the combination of two diagnostic tests, accounting for the likely nonindependence of the tests.

METHODS

A novel multicomponent framework was developed to synthesize information available on different parameters in the model. This allows data to be included from studies evaluating single tests or both tests. Different likelihoods were specified for the different sources of data and linked by means of common parameters. The framework was applied to evaluate the diagnostic accuracy of the Ddimer test and the Wells score for deep vein thrombosis, and the results were compared with those of a model in which independence of tests was assumed. All models were evaluated by using Bayesian Markov chain Monte Carlo simulation methods.

RESULTS

The results showed the importance of allowing for the (likely) nonindependence of tests in the meta-analysis model when evaluating a combination of diagnostic tests. The analysis also highlighted the relatively limited impact of those studies that evaluated only one of the two tests of interest.

CONCLUSIONS

The models developed allowed the assumption of independence between diagnostic tests to be relaxed while combining a broad array of relevant information from disparate studies. The framework also raises questions regarding the utility of studies limited to the evaluation of single diagnostic tests.

Diagnostic accuracy using diffusion tensor imaging in the diagnosis of ALS: a meta-analysis

RATIONALE AND OBJECTIVES

A number of studies have reported decreases in fractional anistropy (FA) in amyotrophic lateral sclerosis using diffusion tensor imaging (DTI). The purpose of this study was to perform a meta-analysis in order to estimate the diagnostic test accuracy measures of DTI for the diagnosis of amyotrophic lateral sclerosis (ALS).

MATERIALS AND METHODS

We searched MEDLINE (1966-April 2011), EMBASE (1999-April 2011), CINAHL (1999-April 2011), and Cochrane (2005-April 2011) databases to identify studies that measured FA in ALS subjects. Human, single-center studies using a DTI region of interest (ROI) or tractography techniques were used to compare FA values along the brain corticospinal tracts between ALS subjects and healthy controls. There were no language restrictions. Independent extraction of articles by 2 authors using predefined data fields including study quality indicators. We identified 30 case-control studies that used region of interest or tractography DTI techniques. We applied binormal receiver operative characteristic (ROC) curve analysis to assign specificity and sensitivity for each study. We applied the bivariate mixed-effects regression model using the Markov Chain Monte Carlo Simulation to calculate summary estimates for the sensitivity and specificity. We used the metan module in Stata, version 11.0, to calculate the area under the ROC curve, diagnostic odds ratio and the test effectiveness summary estimates.

RESULTS

The pooled sensitivity was 0.65 (95% CI 0.61-0.69); the pooled specificity, 0.67 (95% CI 0.63-0.72); the pooled diagnostic odds ratio, 1.88 (95% CI 1.46-2.30); the pooled test effectiveness, 1.04 (95% CI 0.81-1.27); and the pooled area under the ROC curve, 0.76 (95% CI 0.71-0.81). Subanalyses comparing magnetic resonance imaging (MRI) field strength (1.5T vs. 3.0T) and brain location (corticospinal tract average vs. internal capsule) revealed no significant differences in the test accuracy measures. Reference standard used for the diagnosis of ALS was the El Escorial criteria. There was at least moderate heterogeneity between the studies. True study quality is uncertain.

CONCLUSION

The discriminatory capability of DTI to make a diagnosis of ALS is only modest. There were no significant differences in the diagnostic test accuracy summary estimates with respect to MRI field strength or brain location.